With the proliferation and penetration of internet and televised media network in recent years, the affordability and access of media networks has concurrently grown. With such penetration and public viewership, advertisers have shifted to the internet space and televised media networks for promoting their products and services. The demand for ad space has increased in the televised media networks concurrently with an increase in number of broadcasted channels. These advertisers, publishers and content distributors need to closely monitor airing of advertisements across multiple broadcasted television channels and socials networks. The need for faster ad analytics across multiple channels and platforms is witnessed with adoptions of competitive bidding between advertisers for booking ad space.
A television broadcast essentially consists of videos of scheduled programs and sponsored advertisements. Each advertisement video is generally scheduled to run for 10 to 35 seconds approximately on multiple channels at different or same time. The advertisements are provided by advertisers to run in between the scheduled broadcast of the program on each channel. Traditionally, these advertisements are either detected manually by assigning each user for each channel to records ads related data. In technological approaches, the use of supervised detection of key features of the ad in the stream of the broadcast is performed. The approach focuses on detection of advertisements by extracting and analyzing digital audio fingerprints. The audio fingerprints are probabilistically matched with similar fingerprints in a master database. The probabilistic match is analyzed for positive validation of the airing media as an ad.
The present solutions have several disadvantages. The present solutions are inefficient for removing redundancy of false positives in ad detection. The use of audio fingerprints for detecting the ad increases the error rate with the use of similar audio tones and fingerprints in non-ad content. The time for detection of the advertisement also increases with the usage of computationally inefficient and error prone techniques of supervised audio fingerprinting. The increase in detection time affects the synchronization and operations of cross platform ad campaigns. These solutions detect same ad with the different language of communication on different native language channels as different ads. These solutions are not language or completely platform agnostic. In addition, these solutions lack the precision and accuracy to differentiate programs from advertisements.
In light of the above stated discussion, there is a need for a method and system which overcomes the above stated disadvantages.